The performance of tracking and classifications with data from sensors at different locations is greatly influenced by the residual sensor biases. Thus the effectiveness of the sensor registration processing is critical for accurate sensor fusion. Typically, when measurement (or sensor track data) is transmitted from the source platform to a sensor fusion processor, that data is transformed to the reference frame used in the processing and adjusted using the estimated biases. The accuracy of the estimated biases depends on the type of data used to estimate the biases. One of the significant sources of residual registration biases in the uncertainty of the location of each sensor. In establishing an estimate of the location of each sensor, there are a number of methods for estimating the biases depending on how much data is shared between sensor platforms. This paper provides the results of simple analyses to show comparison of the potential bias estimation accuracy that is obtained for each of a number of methods for estimating the biases in sensor location. These methods are also applicable to estimating the misalignment of the sensor axes.